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Informatica Economic vol. 15, no. 1/2011 183

A Stock Trading Algorithm Model Proposal, based on Technical Indicators

Signals

Darie MOLDOVAN, Mircea MOCA, tefan NICHI

Business Information Systems Dept.

Babe-Bolyai University of Cluj-Napoca

{Darie.Moldovan, Mircea.Moca, Stefan.Nitchi}@econ.ubbcluj.ro

The algorithmic stock trading has developed exponentially in the past years, while the

automatism of the technical analysis was the main research are for implementing the

algorithms. This paper proposes a model for a trading algorithm that combines the signals

from different technical indicators in order to provide more accurate trading signals.

Keywords: Decision Support System, Trading Algorithm, Technical Analysis

IntroductionThe use of technical analysis indicators in

decision making for stock investments stays

a controversial subject, being appreciated by

some investors, but rejected by others [4].

While professionals and researchers from the

academic world developed new methods and

indicators, live or simulated tests are needed

to validate them [5].

The price prediction is a very complex issue,

and selecting the right technical indicators

for the analysis of a particular stock is one ofthe first preoccupations of the investors that

use the technical analysis. One difficulty is

the tuning of the parameters of these

indicators in a way that makes their signals

correct in a percentage as high as possible

[1]. While the behavior of the stocks is

different from one to another and changes

during time, the choice of parameters values

becomes a difficult task without the help of

an advanced computational method.

The data mining methods are considered tobe a smart choice for selecting the right

technical indicators, allowing tests on very

large datasets (an essential condition,

regarding the large volume of financial data

available) and many combinations of

parameters values, combining daily, weekly

or monthly prices for tests [2] [5].

Our objective is to propose a methodologythat combines different technical indicators,

based on tests conducted over data sets

gathered from the international or local stockmarkets, and obtaining buy or sell signals

with an improved accuracy, compared to theresults obtained using the use of a singular

indicator, comparing the results with other

research conducted.

The reminder of the paper is structured as

follows. We describe the general conditions

of trading algorithms development in section

2. In section 3 we describe our proposed

methodology for designing a trading

algorithm based on signals from three

different technical analysis indicators.

Section 4 is for conclusions and furtherdevelopments discussion.

2 Related workMost of the studies regarding the trading

algorithms are conducted either as the design

of an agent that works in a simulated

environment, proving their efficiency on the

test data or based on other studies, trying to

prove a superior approach of the problem

described.

In the past years algorithmic trading hasbecome a strong preoccupation for all the

major investors of the financial market,starting 2004 the American markets being

dominated by this kind of trading [6]. Thefirst reason for this trend is the certainty that

the algorithm will follow with perfectconsistency strict rules. Human traders

usually follow trading rules, but become

uncertain at some point that something that

worked in the past will work this time too,

and will make some changes, causing an

important damage to the trading strategy.

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184 Informatica Economic vol. 15, no. 1/2011

A condition for the successful

implementation of a trading algorithm is that

all the processes involved to be automated.

From the gathering of the market data to

sending the orders to the market must bedone automatically, any delay causing a loss

of accuracy. The success of an algorithm,

besides the trading strategy, depends on the

infrastructure and the programming language

used. The reaction speed of the algorithm to

the incoming market data, the processing and

the response are all key factors for the

success or failure. The faster the algorithms

react to live data, the better the strategy

proposed will be applied.

The scope of the algorithm is to take profitfrom the price changes of the stock traded,

considering precise rules, considering themarket data (price and/or volume traded) and

offering buy or sell signals.The main types of trading algorithms are

considering momentum, relative-value ormarket microstructure. The first category

tries to determine trends, based on pricemovements, considering different statistical

indicators, the most common being theaverage. They are considered to perform

better on markets that have a persistent trendin time. Our approach is for this type of

algorithms, as will be discussed later.Another type of algorithms is the one that

compares the evolution of one or more

financial instruments and, based on this, buys

the ones considered undervalued and sells the

overvalued ones.

The algorithms based on the microstructure

of the market will consider the order bookinformation, processing information that

otherwise is difficult to keep and observe by

a human trader and develop a time-effective

strategy.

According to Russell [8] the performance of

a trading algorithm can be tested by three

different approaches: the first is when one

will use a given probability distribution for

the stock prices the Bayesian method; the

competitive analysis assumes the worst case

possible and considers uncertainty for theprices; the other approach is to test the

algorithms on a simulated environment,

using historic data collected.

The signal indicator considered in our model

proposal is a combination of three

momentum indicators used by the technicalanalysis and the benchmark will be if a signal

aggregation will lead to better results than the

signals gathered individually from the

indicators.

The three indicators are MACD(Moving

Average Convergence-Divergence),

ROC(Price Rate of Change) and STS

(Stochastic Oscillator).

MACD is a widely used indicators andtracks the changes in strength, direction,

momentum and direction of a trend. It iscalculated considering the Exponential

Moving Average(EMA) for two different

periods and compare them.

The formula for calculating an EMA at a

certain point is the following:

where is a constant smoothing factor

expressed like a percent or as the number

of periods .

Generally,

EMA=

The weighting factor in each data point(p) is decreasing exponentially, so the

older the data point, the less influence willhave in the result.

Next, the MACD formula is the

following:MACD = , where a < b.

The trade signals are given when the

EMA for the shorter period increases at ahigher value than the longer EMA

( ) - a buy signal - or the

shorter EMA becomes smaller than the

longer EMA .

The Price Rate of Change (ROC) indicatoris an oscillator that calculates themovement of the price between the


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current time price and the one of n periods

of time before.

The calculation formula is the following:

or the relative value:

Where t is the current time and n is the

number of periods of time in the back.

The ROC signals when a certain stock is

overbought or oversold, the tradingsignals occurring when a divergence

appear against the current price evolution.

The Stochastic Oscillator measures themomentum of the market by considering

the trading range from a certain period oftime.

For the calculation we use the followingformulas:

where C represents the closing price of the

stock, L the lowest price for the period,

while is the maximum price.

%D = 3 period MA(%K),

Where MA(%K) is the moving average of

%K.

3 Proposed methodologyThe algorithm gets in touch with two

independent entities and an efficient

communication between them must be

assured. First the database from which the

data for analysis is gathered in real time. This

is a one way communication, from the

database to the system. An important issue is

the consistency of the data, missing orunformatted data is not acceptable, the

technical indicators being very sensitive.

The other entity is the market(e.g. Stock

Exchange). A mutual communication is

needed in this case. The system sends trading

orders to the market and the market sends

responses whether they were executed or not.

A very stable and fast connection is needed

between the two, the execution speed being a

very important factor for the success of a

trading algorithm, sometimes the precisionmust be of milliseconds [7].

Inside the system, the trading algorithm is thecore. All other processes send their signals to

the algorithm, which reacts depending on thecalculations made, deciding whether to send

orders to the market, close the open positionsor not to react at all, just wait for the data to

change so new information becomesavailable.

The system will perform additional tasks, inaddition to the trading signal aggregation.

These tasks are the open positionsmanagement and risk management.

Figure 1 shows the integration of thealgorithm in the whole system.

Market DataStock

Exchange

Trading Algorithm

Signal 1 computation Signal 2 computation Signal 3 computation

Trading ordersExecution

confirmations

Fig. 1. Integration

The functionality of the system depends notonly on the optimal design, but also on the

programming language used. Russell andYoon[8] consider the .Net framework the


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most appropriate due to its superior

flexibility, scalability compatibility and

interoperability.

Before entering a trading order, the system

must do some validations regarding themanagement of the positions owned and the

risk management.

B/S Signal

No

Open positions

Yes

No

Enter order

YesCheck

exposure limit

Not exceeded

Exceded

Indicators Computation

Fig. 2. Functionality

A very often verification of the trading signal

must be performed (this interval can be

settled depending on the trading strategy,

whether it will be a high frequency trading or

not). If a signal was issued the status of thecurrent open positions must be checked. An

exposure limit that was reached will not

allow an order to get in the market.

Figure 2 shows the flow of the order

validation before entering the market. The

flow is a continuous one, in any of the

situations occurred; the end of the

verification process will mean the beginning

of a new flow, with different parameters

obtained from the indicators calculation

module.The risk management process must also take

place in real time, alongside with theindicators calculation and order management.

The value of the open positions and cashavailable must be carefully correlated with

the terms settled when the algorithm starts,the tuning of parameters like order value,

profit and loss limits depending on testsconducted in a simulated environment but

also considering the liquidity of the market,and different risk levels accepted.

Profit/Loss Calculations

ProfitLoss Limit

ExceededNo

Enter closeposition order

Yes

No

Profit Limit

ExceededYes

Yes

No

Fig.3. Risk management

Figure 3 shows the risk management process.Like all other processes involved in the

system, it is a continuous one, the end of acycle represents the beginning of a new one,


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until the algorithm stops. The risk

management process is responsible not only

for the loss limitation but also for cashing the

profit. It has the priority 0 in the system,

having the power to stop the entry of a neworder in the market even if the order would

have been valid in other circumstances.

One important issue is the one of tuning the

parameters of the technical indicators. Even

if in practice there are some standard values

for them, the performance of the algorithm

can be much affected by a non-optimum

parameter value chosen. The main factors

that lead to parameters changing are the trend

(when the trend is ascending the indicators

having some particular parameters willperform considerably better than having the

same parameters on a descending trend) andthe stock behavior (depending on volatility,

the parameters must be adjusted).One smart solution to overcome the issue of

choosing among multiple combinations ofparameters values in order to find the

optimum for the data tested is the use of aGenetic Algorithm[9,10]. A greedy algorithm

that would test all the possible combinationwould be very time consuming, even it

would find the best possible solution. TheGenetic Algorithm will probably not find the

best combination, but one very close to it,acceptable considering the need of time

efficiency.

4 Conclusion and future developments

perspectivesThe combination of the three indicators in

getting trading signals was tested only byhuman traders, and the effectiveness of the

strategies needs to be proven on a simulatedenvironment in order to validate the proposed

model.The integration with a Genetic Algorithm is a

highly desirable solution in order to tune up

the parameters of the indicators in a quick

and reliable way, but also can be used in the

discovery of new trading rules.

A combination between automatically

discovered trading rules and othersdiscovered by experts observations could be

a performing way of setting up the automated

trading system.

Considering that every individual has

different risk averseness the risk-return

balance must be controlled, which may leadin a development of an evolutionary trading

system.

A limitation of the state of the art knowledge

about developments in this field is a fact that

could be an obstacle in the study, in most

cases the developers of the algorithms do not

make public the results and the methodology

used.

Acknowledgments

This work was supported by ANCS-CNMP,project number PNII 91049/2007.

References

[1] D. J. Bodas-Sagi, P. Fernndez, J. I.Hidalgo, F.J. Soltero and J.L. Risco-

Martn, Multiobjective optimization oftechnical market indicators. In

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[3] M. Tanaka-Yamawaki, S. Tokuoka.

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[5] G. C. Silaghi, V. Robu. An agent

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[8] S. Russell, V. Yoon. Heterogeneous

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[9] L. Lin, L. Cao. Mining in-depth patterns

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Darie MOLDOVAN is a PhD Student at the Faculty of Economics and

Business Administration, Business Information Systems department at

Babe-Bolyai University of Cluj-Napoca. His doctoral orientation is towards

studying the application of Data Mining in Finance. He is interested in theBusiness Intelligence field, trading algorithms and automated systems for

trading.

Mircea MOCA is a Teaching Assistant and PhD Student at the Faculty ofEconomics and Business Administration, Business Information Systems

department at Babes-Bolyai University of Cluj-Napoca. So far he worked ondeveloping distributed computing systems using structured and unstructured

P2P architectures. His research interests are volunteer computing systems andbuilding MapReduce applications for Desktop Grids.

tefan Ioan NICHI is a Professor, PhD, at the Faculty of Economics and

Business Administration, Business Information Systems department at

Babes-Bolyai University of Cluj-Napoca. He has very strong knowledge in

collaborative systems and databases. His research interests are collaborative

systems for business intelligence and distributed decision support systems.
http://www.ece.utexas.edu/~ramamoor/http://www.cs.utexas.edu/~pstonehttp://www.cs.utexas.edu/~pstonehttp://www.cs.utexas.edu/~pstonehttp://www.cs.utexas.edu/~pstonehttp://www.ece.utexas.edu/~ramamoor/

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A stock trading algoritm.pdf